A recommendation system for recommending a target feature value for a target feature for a target deployment is provided. The recommendation system, for each of a plurality of deployments, collects feature values for the features of that deployment. The recommendation system then generates a model for recommending a target feature value for the target feature based on the collected feature values of the features for the deployments. The recommendation system applies the model to the features of the target deployment to identify a target feature value for the target feature. The recommendation system then provides the identified target feature value as a recommendation for the target feature for the target deployment.

A computer-implemented method and apparatus for monitoring the performance of a plurality of network nodes interconnected in a multi-hop arrangement using at least one node performance assessment threshold is described. A plurality of data sets is obtained. A data set comprises a respective value of a performance metric for each of the plurality of network nodes. Each of the plurality of data sets is classified as normal or abnormal by comparing the respective values of the performance metric of each of the plurality of network nodes to a corresponding normality threshold, thus providing a plurality of classified data sets. The plurality of classified data sets is processed using a machine-learning algorithm in order to derive, for at least one network node of the plurality of network nodes, a node performance assessment threshold indicative of a value of the performance metric of the at least one node at which the plurality of network nodes has a predetermined likelihood of being classified as normal.

A method and apparatus for differentially optimizing a quality of service (QoS) includes: establishing a system model of a multi-task unloading framework; acquiring a mode for users executing a computation task, executing, according to the mode for users executing the computation task, the system model of the multi-task unloading framework; and optimizing a quality of service (QoS) on the basis of a multi-objective optimization method for a multi-agent deep reinforcement learning. According to the present invention, an unloading policy is calculated on the basis of a multi-user differentiated QoS of a multi-agent deep reinforcement learning, and with the differentiated QoS requirements among different users in a system being considered, a global unloading decision is performed according to a task performance requirement and a network resource state, and differentiated performance optimization is performed on different user requirements, thereby effectively improving a system resource utilization rate and a user service quality.

The explanation engine has a set of modules cooperating with each other configured to evaluate layers in a hierarchical architecture of a machine-based reasoning process that uses machine learning. The set of modules cooperate to support an explanation of how the machine-based reasoning process arrived at its reported results of both a final/top level result as well as corresponding intermediate output results. A messaging module of the explanation engine can collect the top-level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process. Multiple layers of reasoning are associated with terminology used in at least one of i) a problem to be solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication.

The present invention relates to methods for accurate downtime calculation. The method comprises filtering an input signal from a system by a closing process and an opening process to generate a first smoothed signal. The method may include applying moving average filtering to the first smoothed signal to generate a second smoothed signal. The method may further include generating the morphology event labels and the average event labels of the signal based on the first and second smoothed signals, and determining the downtime intervals by the event labels.

In one embodiment, a labeling service receives telemetry data for a cluster of endpoint devices in a first network environment. The endpoint devices in the cluster are clustered by a device classification service based on their telemetry data and labeled by a device type classifier of the device classification service as being of an unknown device type. The labeling service obtains a first device type label for the cluster of endpoint devices via a first user interface. The labeling service identifies one or more other network environments in which endpoint devices are located that have similar telemetry data as that of the cluster of endpoint devices. The labeling service obtains device type labels for the cluster of endpoint devices via a selected set of user interfaces from the identified one or more other network environments. The labeling service validates the first device type label for the cluster using the device type labels obtained via the selected set of user interfaces from the identified one or more other network environments.

A method and apparatus utilize a peer-to-peer network of security nodes collectively adhering to a protocol for inter-node communication. The system is comprised a plurality of first security nodes, at least one second security node, and at least one third security node. The plurality of first security nodes receive at least one of pre-trained detection models and rules, monitor at least one of a blockchain and connected devices for malicious behavior based on the received at least one of pre-trained detection models and rules, and report the malicious behavior. The at least one second security node creates and communicates the at least one of pre-trained detection models and rules to the plurality of first security nodes. The at least one third security node is informed by the at least one second security node of the reported malicious behavior.

In a 4G LTE wireless carrier network, network slice instances are instantiated that are configured to provide a configured set of services that are accessible to a controlled set of user devices. A service profile for a user device is identified and analyzed. When the service profile matches a configured set of services for one of the instantiated network slice instances, the user device is enabled to access the matching instantiated network instance. The provisioning of the network slice instances is performed by a dedicated node.

A method, an apparatus, and a system for sending a control request based on a key value configuration. The method includes generating, by a client controller, a control request, wherein having a plurality of configuration parameters, wherein the plurality of configuration parameters comprise a flexible key value, a control object name, and a universally unique identifier (UUID), where the flexible key value identifies a requested control object, and the flexible key value is determined using at least one of the control object name or the UUID, and sending, by the client controller, the control request to a server controller, where the control request indicates to the server controller to configure the control object.

The multi-agent simulation system includes a plurality of agent simulators provided for each of the plurality of agents and a center controller. The plurality of agent simulators are programmed to simulate a state of each of the plurality of agents while causing the plurality of agents to interact with each other by exchanging messages. The center controller is programmed to control a speed ratio of a flow of time in the target world to a flow of time in a real world. Each of the plurality of agent simulators calculates an index value corresponding to a remainder time rate. The remainder time rate is a rate of a remainder time to an update time interval for updating a state of a target agent to be simulated. The center controller controls the speed ratio based on the index value calculated by each of the plurality of agent simulators.